Multi-objective optimization method based on improved gravitation search algorithm

Abstract

The invention discloses a multi-objective optimization method based on an improved gravitation search algorithm. According to the algorithm, a memory strategy is introduced into a universal gravitation search algorithm, so that particle swarm information and information of previous generations and next generations of particles are shared, the global search capability and the local search capability of the particles are balanced, and the premature convergence problem is solved. On this basis, a diversity enhancement mechanism is introduced into the algorithm, namely, particle speed and position are controlled each iteration, so that the diversity loss is relieved, the particle diversity is improved, and diversity and distributivity of non-dominated solution sets are enhanced. Therefore, by means of the multi-objective optimization method based on the improved gravitation search algorithm, the phenomenon that multi-objective optimization is caught in a local extremum can be effectively avoided, and convergence, diversity and distributivity of non-dominated solutions are remarkably improved when the gravitation search algorithm is applied to the field of multi-objective optimization.

Description

technical field [0001] The invention relates to a multi-objective optimization method based on an improved gravitational search algorithm. Background technique [0002] Multi-objective optimization problems play a very important role in both scientific research and engineering applications. The essential difference from the single-objective optimization problem is that its solution is not unique, but there is a set of optimal solutions composed of many Pareto optimal solutions, and each element in the set is called Pareto optimal solution or non-dominated untie. [0003] Because in the multi-objective optimization algorithm, the calculation and search of the global optimal particle (selection of guided particles) has an important impact on the convergence and distribution of the solution in the multi-objective optimization, the evolutionary algorithm with outstanding global optimization ability is favored Applied to the field of multi-objective optimization. At present, t...

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Problems solved by technology

[0005] However, when the gravitational search algorithm is applied to multi-objective optimization, some of its own shortcomings lead to low quality of the global optimal particle in the algorithm, and the effect of multi-objective optimization needs to be improved

First of all, in the gravitational search algorithm, only the current position information plays a role in the iterative update process, that is, the algorithm is an algorithm that lacks memory, which leads to no information exchange between the upper and lower generations of the population, and it is easy to fall into premature convergence

On the other hand, due to the high particle speed in the gravitational search algorithm, all of them move towards the particles with large mass, and the convergence is very fast, so the diversity of the population decreases rapidly, that is, the diversity is lost rapidly, and the diversity of non-dominated solutions cannot be guaranteed. and distribution

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